{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imputation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create data with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    {\n",
    "        'gender': np.random.binomial(1, .6, 100),\n",
    "        'height': np.random.normal(0, 10, 100), \n",
    "        'noise': np.random.normal(0, 2, 100), \n",
    "    }\n",
    ")\n",
    "\n",
    "df['height'] = df['height'] + df['gender'].apply(\n",
    "    lambda g: 150 if g else 180\n",
    ")\n",
    "df['height (with 75% NaN)'] = df['height'].apply(\n",
    "    lambda x: x if np.random.binomial(1, .25, 1)[0] else np.nan\n",
    ")\n",
    "df['height (with 10% NaN)'] = df['height'].apply(\n",
    "    lambda x: x if np.random.binomial(1, .9, 1)[0] else np.nan\n",
    ")\n",
    "df['weight'] = df['height'] + df['noise'] - 110 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>height (with 75% NaN)</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>38.614799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>46.983233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>36.125179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>64.960558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   height (with 75% NaN)     weight\n",
       "0                    NaN  38.614799\n",
       "1                    NaN  46.983233\n",
       "2                    NaN  36.125179\n",
       "3                    NaN  64.960558"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(4).reset_index()[\n",
    "    [\n",
    "        'height (with 75% NaN)',\n",
    "        'weight'\n",
    "    ]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>noise</th>\n",
       "      <th>height (with 75% NaN)</th>\n",
       "      <th>height (with 10% NaN)</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>151.423285</td>\n",
       "      <td>0.016064</td>\n",
       "      <td>NaN</td>\n",
       "      <td>151.423285</td>\n",
       "      <td>41.439349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0</td>\n",
       "      <td>188.913643</td>\n",
       "      <td>-2.352001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>188.913643</td>\n",
       "      <td>76.561642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>140.366754</td>\n",
       "      <td>1.668068</td>\n",
       "      <td>140.366754</td>\n",
       "      <td>140.366754</td>\n",
       "      <td>32.034822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>152.845533</td>\n",
       "      <td>-3.242559</td>\n",
       "      <td>152.845533</td>\n",
       "      <td>152.845533</td>\n",
       "      <td>39.602974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1</td>\n",
       "      <td>150.726551</td>\n",
       "      <td>-1.627827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.098724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    gender      height     noise  height (with 75% NaN)  \\\n",
       "21       1  151.423285  0.016064                    NaN   \n",
       "70       0  188.913643 -2.352001                    NaN   \n",
       "28       1  140.366754  1.668068             140.366754   \n",
       "4        1  152.845533 -3.242559             152.845533   \n",
       "81       1  150.726551 -1.627827                    NaN   \n",
       "\n",
       "    height (with 10% NaN)     weight  \n",
       "21             151.423285  41.439349  \n",
       "70             188.913643  76.561642  \n",
       "28             140.366754  32.034822  \n",
       "4              152.845533  39.602974  \n",
       "81                    NaN  39.098724  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                   0.00\n",
       "height (with 75% NaN)    0.78\n",
       "weight                   0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\n",
    "    [\n",
    "        'gender',\n",
    "        'height (with 75% NaN)',\n",
    "        'weight',\n",
    "    ]\n",
    "].isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/ipykernel_launcher.py:17: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(15, 6))\n",
    "\n",
    "df.plot(\n",
    "    kind='scatter',\n",
    "    x='gender',\n",
    "    y='weight',\n",
    "    ax=axs[0]\n",
    ")\n",
    "\n",
    "df.plot(\n",
    "    kind='scatter',\n",
    "    x='height',\n",
    "    y='weight',\n",
    "    ax=axs[1]\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Imputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "df['height (75% zero imputed)'] = df['height (with 75% NaN)'].fillna(0)\n",
    "\n",
    "imp = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "df['height (10% mean imputed)'] = imp.fit_transform(df[['height (with 10% NaN)']])[:, 0]\n",
    "df['height (75% mean imputed)'] = imp.fit_transform(df[['height (with 75% NaN)']])[:, 0]\n",
    "\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer\n",
    "imp = IterativeImputer(missing_values=np.nan)\n",
    "df['height (75% iterative imputed)'] = imp.fit_transform(df[['height (with 75% NaN)', 'gender']])[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import MissingIndicator\n",
    "\n",
    "imp = MissingIndicator(missing_values=np.nan)\n",
    "df['height (is missing?)'] = imp.fit_transform(df[['height (with 75% NaN)']])[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    78.000000\n",
       "mean     52.516244\n",
       "std      18.652964\n",
       "min       9.965745\n",
       "25%      38.180359\n",
       "50%      48.570769\n",
       "75%      69.953581\n",
       "max      96.675027\n",
       "Name: weight, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\n",
    "    df['height (75% zero imputed)'] == 0\n",
    "]['weight'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing the effect of the Imputation strategies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/ipykernel_launcher.py:37: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 4, figsize=(15, 6))\n",
    "\n",
    "df.plot(\n",
    "    title='Complete data',\n",
    "    kind='scatter',\n",
    "    x='height',\n",
    "    y='weight',\n",
    "    ax=axs[0]\n",
    ")\n",
    "\n",
    "df.plot(\n",
    "    title='75% of height data missing\\nZero imputed',\n",
    "    kind='scatter',\n",
    "    x='height (75% zero imputed)',\n",
    "    y='weight',\n",
    "    ax=axs[1]\n",
    ")\n",
    "\n",
    "\n",
    "df.plot(\n",
    "    title='75% of height data missing\\nMean imputed',\n",
    "    kind='scatter',\n",
    "    x='height (75% mean imputed)',\n",
    "    y='weight',\n",
    "    ax=axs[2]\n",
    ")\n",
    "\n",
    "df.plot(\n",
    "    title='10% of height data missing\\nMean imputed',\n",
    "    kind='scatter',\n",
    "    x='height (10% mean imputed)',\n",
    "    y='weight',\n",
    "    ax=axs[3]\n",
    ")\n",
    "\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/ipykernel_launcher.py:28: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(15, 6))\n",
    "\n",
    "df.plot(\n",
    "    title='Complete data',\n",
    "    kind='scatter',\n",
    "    x='height',\n",
    "    y='weight',\n",
    "    ax=axs[0]\n",
    ")\n",
    "\n",
    "df.plot(\n",
    "    title='75% of height data missing',\n",
    "    kind='scatter',\n",
    "    x='height (75% mean imputed)',\n",
    "    y='weight',\n",
    "    ax=axs[1]\n",
    ")\n",
    "\n",
    "df.plot(\n",
    "    title='75% of height data missing',\n",
    "    kind='scatter',\n",
    "    x='height (75% iterative imputed)',\n",
    "    y='weight',\n",
    "    ax=axs[2]\n",
    ")\n",
    "\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imputation + Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original data: [-0.04385966  0.99726604] -109.46383195458489\n",
      "MSE: 4.703031303548416\n",
      "Zero imputed data: [-2.20166259e+01  7.93424023e-03] 65.26258472707451\n",
      "MSE: 119.38533651345102\n",
      "Mean imputed data: [-19.73272337   0.53504387] -20.992158726755328\n",
      "MSE: 113.07382748799405\n",
      "Mean imputed data + indicator: [-0.53022893  0.96752543] -104.9616415654786\n",
      "MSE: 86.40678889058803\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.tree import export_text\n",
    "from sklearn.metrics import (\n",
    "    mean_squared_error, mean_absolute_error, median_absolute_error, r2_score\n",
    ")\n",
    "\n",
    "reg = Ridge(alpha=10)\n",
    "# reg = DecisionTreeRegressor()\n",
    "x, y = df[['gender', 'height']], df['weight']\n",
    "reg.fit(x, y)\n",
    "print('Original data:', reg.coef_, reg.intercept_)\n",
    "# print('Original data:', reg.feature_importances_)\n",
    "print('MSE:', mean_squared_error(y, reg.predict(x)))\n",
    "\n",
    "reg = Ridge(alpha=10)\n",
    "# reg = DecisionTreeRegressor()\n",
    "x, y = df[['gender', 'height (75% zero imputed)']], df['weight']\n",
    "reg.fit(x, y)\n",
    "print('Zero imputed data:', reg.coef_, reg.intercept_)\n",
    "# print('Mean imputed data + indicator:', reg.feature_importances_)\n",
    "print('MSE:', mean_squared_error(y, reg.predict(x)))\n",
    "\n",
    "reg = Ridge(alpha=10)\n",
    "# reg = DecisionTreeRegressor()\n",
    "x, y = df[['gender', 'height (75% mean imputed)']], df['weight']\n",
    "reg.fit(x, y)\n",
    "print('Mean imputed data:', reg.coef_, reg.intercept_)\n",
    "# print('Mean imputed data + indicator:', reg.feature_importances_)\n",
    "print('MSE:', mean_squared_error(y, reg.predict(x)))\n",
    "\n",
    "reg = Ridge(alpha=10)\n",
    "# reg = DecisionTreeRegressor()\n",
    "x, y = df[['gender', 'height (75% iterative imputed)',]], df['weight']\n",
    "reg.fit(x, y)\n",
    "print('Mean imputed data + indicator:', reg.coef_, reg.intercept_)\n",
    "# print('Mean imputed data + indicator:', reg.feature_importances_)\n",
    "print('MSE:', mean_squared_error(y, reg.predict(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original data: [0.99853275] -109.69600876293777\n",
      "MSE: 4.703320514177929\n",
      "Zero imputed data: [-0.0038457] 52.024554187577756\n",
      "MSE: 329.9232879169621\n",
      "Mean imputed data: [0.98099073] -104.1690320693751\n",
      "MSE: 270.36113636930327\n",
      "Mean imputed data + indicator: [0.98352832] -107.89014959803853\n",
      "MSE: 86.46447393318422\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.tree import export_text\n",
    "from sklearn.metrics import (\n",
    "    mean_squared_error, mean_absolute_error, median_absolute_error, r2_score\n",
    ")\n",
    "\n",
    "reg = Ridge()\n",
    "# reg = DecisionTreeRegressor()\n",
    "x, y = df[['height']], df['weight']\n",
    "reg.fit(x, y)\n",
    "print('Original data:', reg.coef_, reg.intercept_)\n",
    "# print('Original data:', reg.feature_importances_)\n",
    "print('MSE:', mean_squared_error(y, reg.predict(x)))\n",
    "\n",
    "reg = Ridge()\n",
    "# reg = DecisionTreeRegressor()\n",
    "x, y = df[['height (75% zero imputed)']], df['weight']\n",
    "reg.fit(x, y)\n",
    "print('Zero imputed data:', reg.coef_, reg.intercept_)\n",
    "# print('Mean imputed data + indicator:', reg.feature_importances_)\n",
    "print('MSE:', mean_squared_error(y, reg.predict(x)))\n",
    "\n",
    "reg = Ridge()\n",
    "# reg = DecisionTreeRegressor()\n",
    "x, y = df[['height (75% mean imputed)']], df['weight']\n",
    "reg.fit(x, y)\n",
    "print('Mean imputed data:', reg.coef_, reg.intercept_)\n",
    "# print('Mean imputed data + indicator:', reg.feature_importances_)\n",
    "print('MSE:', mean_squared_error(y, reg.predict(x)))\n",
    "\n",
    "reg = Ridge()\n",
    "# reg = DecisionTreeRegressor()\n",
    "x, y = df[['height (75% iterative imputed)',]], df['weight']\n",
    "reg.fit(x, y)\n",
    "print('Mean imputed data + indicator:', reg.coef_, reg.intercept_)\n",
    "# print('Mean imputed data + indicator:', reg.feature_importances_)\n",
    "print('MSE:', mean_squared_error(y, reg.predict(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tarek/anaconda3/envs/scikitbook/lib/python3.6/site-packages/ipykernel_launcher.py:75: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(15, 6))\n",
    "\n",
    "df.plot(\n",
    "    title='Complete data',\n",
    "    kind='scatter',\n",
    "    x='height',\n",
    "    y='weight',\n",
    "    ax=axs[0]\n",
    ")\n",
    "\n",
    "x, y = df[['height']], df['weight']\n",
    "reg = Ridge()\n",
    "reg.fit(x, y)\n",
    "axs[0].plot(\n",
    "    [\n",
    "        df['height'].min(),\n",
    "        df['height'].max()\n",
    "    ], \n",
    "    [\n",
    "        reg.intercept_ + reg.coef_[0] * df['height'].min(),\n",
    "        reg.intercept_ + reg.coef_[0] * df['height'].max()\n",
    "    ], \n",
    "    'k--',\n",
    "    alpha=0.5,\n",
    ")\n",
    "\n",
    "df.plot(\n",
    "    title='75% of height data missing\\nZero imputed',\n",
    "    kind='scatter',\n",
    "    x='height (75% zero imputed)',\n",
    "    y='weight',\n",
    "    ax=axs[1]\n",
    ")\n",
    "x, y = df[['height (75% zero imputed)']], df['weight']\n",
    "reg = Ridge()\n",
    "reg.fit(x, y)\n",
    "axs[1].plot(\n",
    "    [\n",
    "        df['height (75% zero imputed)'].min(),\n",
    "        df['height (75% zero imputed)'].max()\n",
    "    ], \n",
    "    [\n",
    "        reg.intercept_ + reg.coef_[0] * df['height (75% zero imputed)'].min(),\n",
    "        reg.intercept_ + reg.coef_[0] * df['height (75% zero imputed)'].max()\n",
    "    ], \n",
    "    'k--',\n",
    "    alpha=0.5,\n",
    ")\n",
    "\n",
    "\n",
    "df.plot(\n",
    "    title='75% of height data missing\\nMean imputed',\n",
    "    kind='scatter',\n",
    "    x='height (75% mean imputed)',\n",
    "    y='weight',\n",
    "    ax=axs[2]\n",
    ")\n",
    "x, y = df[['height (75% mean imputed)']], df['weight']\n",
    "reg = Ridge()\n",
    "reg.fit(x, y)\n",
    "axs[2].plot(\n",
    "    [\n",
    "        df['height (75% mean imputed)'].min(),\n",
    "        df['height (75% mean imputed)'].max()\n",
    "    ], \n",
    "    [\n",
    "        reg.intercept_ + reg.coef_[0] * df['height (75% mean imputed)'].min(),\n",
    "        reg.intercept_ + reg.coef_[0] * df['height (75% mean imputed)'].max()\n",
    "    ], \n",
    "    'k--',\n",
    "    alpha=0.5,\n",
    ")\n",
    "\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
